Pith Number

pith:BCWOJTQQ

pith:2023:BCWOJTQQ3S6CLOPQTE7P5E3XV2

not attested not anchored not stored refs resolved

HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face

Dongsheng Li, Kaitao Song, Weiming Lu, Xu Tan, Yongliang Shen, Yueting Zhuang

Large language models like ChatGPT can coordinate existing AI models to solve sophisticated multi-modal tasks by planning and selecting them via language descriptions.

arxiv:2303.17580 v4 · 2023-03-30 · cs.CL · cs.AI · cs.CV · cs.LG

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\usepackage{pith}
\pithnumber{BCWOJTQQ3S6CLOPQTE7P5E3XV2}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

By leveraging the strong language capability of ChatGPT and abundant AI models in Hugging Face, HuggingGPT can tackle a wide range of sophisticated AI tasks spanning different modalities and domains and achieve impressive results in language, vision, speech, and other challenging tasks.

C2weakest assumption

That ChatGPT can reliably perform task planning and select appropriate models from their function descriptions without frequent errors that would break the overall solution.

C3one line summary

HuggingGPT is an agent system where ChatGPT plans and orchestrates calls to Hugging Face models to solve complex multi-modal AI tasks.

References

62 extracted · 62 resolved · 12 Pith anchors

[1] Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-V oss, Gretch 2020

[2] Training language models to follow instructions with human feedback 2022 · arXiv:2203.02155

[3] PaLM: Scaling Language Modeling with Pathways 2022 · arXiv:2204.02311

[4] OPT: Open Pre-trained Transformer Language Models 2022 · arXiv:2205.01068

[5] Glm-130b: An Open Bilingual Pre-trained Model 2023

Formal links

3 machine-checked theorem links

Cited by

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Receipt and verification

First computed	2026-05-18T03:27:44.464076Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

08ace4ce10dcbc25b9f0993efe9377aeb5a54721c17707096d3b74388b8b909e

Aliases

arxiv: 2303.17580 · arxiv_version: 2303.17580v4 · doi: 10.48550/arxiv.2303.17580 · pith_short_12: BCWOJTQQ3S6C · pith_short_16: BCWOJTQQ3S6CLOPQ · pith_short_8: BCWOJTQQ

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/BCWOJTQQ3S6CLOPQTE7P5E3XV2 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 08ace4ce10dcbc25b9f0993efe9377aeb5a54721c17707096d3b74388b8b909e

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "62acc73266a99899bf6553ee8bb2946c3ab45cc71a2f7722c3a1de9f806cb9c1",
    "cross_cats_sorted": [
      "cs.AI",
      "cs.CV",
      "cs.LG"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CL",
    "submitted_at": "2023-03-30T17:48:28Z",
    "title_canon_sha256": "87c03022be7538b6640bc8aabeffa8e484de18c7a5814473f7060c4970dc07aa"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2303.17580",
    "kind": "arxiv",
    "version": 4
  }
}